1. Field of the Invention
The present invention relates to an image processing apparatus for quantizing image data into multi-level data such as ternary or quaternary data.
2. Related Background Art
Conventional halftone processing methods which are employed in image processing apparatuses, such as facsimile machines or digital copiers, include the error diffusion method and the average density approximation method.
The former method has been proposed, for example, from Page 36 to Page 37 in "An Adaptive Algorithm for Spatial Gray Scale", SID 75 Digest by R. Floyd & L. Steinberg. In this method, multi-level image data on an object pixel is binarized (converted into the highest density level or lowest density level). Thereafter, an error between the thus-obtained binary level and the multi-level image data which is not yet binarized is weighted, and this weighted error is added to data on the pixels in the vicinity of the object pixel.
The present assignee has already filed applications regarding the error diffusion method under several patent applications, now U.S. Pat. Nos. 4,876,610 (Ohsawa et al.), 4,878,125 (Katayama et al.) 5,008,950 (Katayama et al.), 4,958,236 (Nagashima et al.), 4,958,218 (Katayama et al.), 4,975,786 (Katayama et al.), and 4,958,238 (Katayama et al.) and pending U.S. application Ser. Nos. 07/192,601 and 07/270,809.
The latter method is disclosed in, for example, Japanese Unexamined Patent Publication (Kokai) No. 57-104369. In this method, weighted average values of the pixels in the vicinity of an object pixel when the object pixel is binarized into black and white levels are respectively obtained using the binary data on the pixels in the vicinity of the object pixel, and the image data on the object pixel is binarized using the average value of these two average values as a threshold.
The aforementioned error diffusion method involves correction of an error between image data which is input and image data which is output. As a consequence, the density of the input image and of the output image can be stored, and this enables provision of images which are excellent in both resolution and gradation.
However, in this error diffusion method, a large amount of data must be two-dimensionally calculated in order to correct the error between the input image data and the output image data, and this makes the hardware configuration very complicated.